{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a712204f-4768-4aca-bd0f-52131ef12e08",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-20 09:55:24.012053: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-06-20 09:55:24.037917: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-20 09:55:24.496489: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import nibabel as nib\n",
    "import cv2\n",
    "\n",
    "import SimpleITK as sitk\n",
    "from PIL import Image\n",
    "from collections import defaultdict\n",
    "\n",
    "from monai.transforms import (\n",
    "    AsDiscrete,\n",
    "    AsDiscreted,\n",
    "        EnsureChannelFirstd,\n",
    "    Compose,\n",
    "    LoadImaged,\n",
    "    RandSpatialCropd,\n",
    "    CenterSpatialCropd,\n",
    "    EnsureChannelFirstd,\n",
    "    RandFlipd,\n",
    "    Resized,\n",
    "    ScaleIntensityRangePercentilesd,\n",
    "    NormalizeIntensityd,\n",
    "    Identity,\n",
    "    EnsureTyped,\n",
    "MapTransform\n",
    ")\n",
    "\n",
    "from glob import glob\n",
    "import skimage.io as io\n",
    "\n",
    "\n",
    "from monai.handlers.utils import from_engine\n",
    "# https://docs.monai.io/en/stable/networks.html#nets\n",
    "from monai.networks.nets import UNet,AttentionUnet, DynUNet, SegResNet, VNet, SegResNetVAE, UNETR\n",
    "from monai.networks.layers import Norm\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.losses import DiceLoss\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.data import CacheDataset, DataLoader, Dataset, decollate_batch\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import aim\n",
    "from aim.pytorch import track_gradients_dists, track_params_dists\n",
    "import matplotlib.pyplot as plt\n",
    "import tempfile\n",
    "import shutil\n",
    "import os\n",
    "np.set_printoptions(threshold=np.inf)\n",
    "random.seed(7777)\n",
    "np.random.seed(7777)\n",
    "\n",
    "NUM_PREFETCH = 10\n",
    "RANDOM_SEED = 123\n",
    "\n",
    "\n",
    "class DataLoaderCamus(Dataset):\n",
    "    def __init__(self, dataset_path, input_name, gt_name, stage, single_frame=True,\n",
    "                 img_res=(272, 272), img_crop=(256, 256), seg_parts=True, train_ratio=0.8, valid_ratio=0.2):\n",
    "        self.dataset_path = dataset_path\n",
    "        self.input_name = input_name\n",
    "        self.gt_name = gt_name\n",
    "        self.spatial_size = img_res[0]\n",
    "        self.crop_size = img_crop[0]\n",
    "        self.single_frame = single_frame\n",
    "        self.seg_parts = seg_parts\n",
    "        self.is_train = True if stage == 'train' else False\n",
    "\n",
    "        self.transform = self.get_transform(self.is_train)\n",
    "        \n",
    "        patients = []\n",
    "        for id, dir in enumerate(sorted(glob(os.path.join(self.dataset_path, 'database_nifti/', '*')))):\n",
    "            if not os.listdir(dir):\n",
    "                continue\n",
    "            else:\n",
    "                patients.append(dir)\n",
    "\n",
    "        random.Random(RANDOM_SEED).shuffle(patients)\n",
    "        num = len(patients)\n",
    "        num_train = int(num * train_ratio)\n",
    "        num_valid = int(num_train * valid_ratio)\n",
    "\n",
    "        self.train_patients = patients[num_valid:num_train]\n",
    "        self.valid_patients = patients[:num_valid//2]\n",
    "        self.test_patients = patients[num_valid//2:num_valid]\n",
    "        #self.test_patients = patients[num_train:]\n",
    "        '''\n",
    "        if train_ratio == 1.0:\n",
    "            self.test_patients = glob(os.path.join(self.dataset_path, 'testing', '*'))\n",
    "        '''\n",
    "        if stage == 'train':\n",
    "            self.data_list = self.train_patients\n",
    "            print('#train:', len(self.data_list), self.data_list[:5])\n",
    "        elif stage == 'valid':\n",
    "            self.data_list = self.valid_patients\n",
    "            print('#valid:', len(self.data_list))\n",
    "        elif stage == 'test':\n",
    "            self.data_list = self.test_patients\n",
    "            print('#test:', len(self.data_list))\n",
    "\n",
    "        self.data_length = len(self.data_list)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        mask_index = 0\n",
    "        path = self.data_list[index]\n",
    "        input_path, condition_path = self.get_path(path)\n",
    "        # while not os.path.exists(input_path):\n",
    "        #     index = random.randint(0, self.data_length)\n",
    "        #     path = self.data_list[index]\n",
    "        #     input_path, condition_path = self.get_path(path)\n",
    "        # print(input_path, condition_path)\n",
    "        # input_img = self.read_mhd(input_path, '_gt' in self.input_name)\n",
    "        # condition_img = self.read_mhd(condition_path, True)\n",
    "\n",
    "        if self.seg_parts:\n",
    "            LV = np.where(condition_img == 1, 1, 0)\n",
    "            LA = np.where(condition_img == 3, 1, 0)\n",
    "            condition_img = np.stack([LV, LA], axis=0)\n",
    "\n",
    "        input_dict = self.transform({'images': input_path, 'masks': condition_path})\n",
    "\n",
    "        return input_dict['images'] / 255.0, input_dict['masks'] / 1.0, mask_index, index\n",
    "        # return input_dict\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_list)\n",
    "\n",
    "    def read_mhd(self, img_path, is_gt):\n",
    "        img = io.imread(img_path, plugin='simpleitk').squeeze()\n",
    "        return img\n",
    "\n",
    "    def get_path(self, img_path):\n",
    "        _, patient_id = os.path.split(img_path)\n",
    "        input_path = os.path.join(img_path, '{}_{}.nii.gz'.format(patient_id, self.input_name))\n",
    "        mask_path = os.path.join(img_path, '{}_{}.nii.gz'.format(patient_id, self.gt_name))\n",
    "\n",
    "        return input_path, mask_path\n",
    "\n",
    "    def get_transform(self, is_train):\n",
    "        all_keys = ['images', 'masks']\n",
    "        \n",
    "        if self.single_frame:\n",
    "            spatial_size = (self.spatial_size, self.spatial_size)\n",
    "            crop_size = (self.crop_size, self.crop_size)\n",
    "        else:\n",
    "            spatial_size = (self.spatial_size, self.spatial_size, self.clip_length)\n",
    "            crop_size = (self.crop_size, self.crop_size, self.clip_length)\n",
    "        \n",
    "        if is_train:\n",
    "            \n",
    "            rf0 = RandFlipd(keys=all_keys, prob=0.5, spatial_axis=0)\n",
    "            rf1 = RandFlipd(keys=all_keys, prob=0.5, spatial_axis=1)\n",
    "            rf2 = RandFlipd(keys=all_keys, prob=0.5, spatial_axis=2) if not self.single_frame else None\n",
    "\n",
    "            rf0.set_random_state(0)\n",
    "            rf1.set_random_state(0)\n",
    "            if rf2 is not None:\n",
    "                rf2.set_random_state(0)\n",
    "\n",
    "            transform = Compose([\n",
    "                    LoadImaged(keys=all_keys),\n",
    "                    EnsureChannelFirstd(keys=['images'] if self.seg_parts else all_keys, allow_missing_keys=True),\n",
    "                    Resized(keys=all_keys, spatial_size=spatial_size, allow_missing_keys=True, mode='nearest'),\n",
    "                    RandSpatialCropd(all_keys, crop_size, random_size=False, allow_missing_keys=True),\n",
    "                    #ScaleIntensityRangePercentilesd(keys=all_keys, lower=5, upper=95, b_min=0., b_max=1., allow_missing_keys=True) if len(all_keys)>0 else Identity(),\n",
    "                    #NormalizeIntensityd(keys=all_keys, subtrahend=0.5, divisor=0.5),\n",
    "                    EnsureTyped(keys=all_keys, allow_missing_keys=True),\n",
    "                ])\n",
    "        else:\n",
    "            transform = Compose([\n",
    "                    LoadImaged(keys=all_keys),\n",
    "                    EnsureChannelFirstd(keys=['images'] if self.seg_parts else all_keys, allow_missing_keys=True),\n",
    "                    Resized(keys=all_keys, spatial_size=spatial_size, allow_missing_keys=True, mode='nearest'),\n",
    "                    CenterSpatialCropd(keys=all_keys, roi_size=crop_size, allow_missing_keys=True),\n",
    "                    #ScaleIntensityRangePercentilesd(keys=all_keys, lower=5, upper=95, b_min=0., b_max=1., allow_missing_keys=True) if len(all_keys)>0 else Identity(),\n",
    "                    #NormalizeIntensityd(keys=all_keys, subtrahend=0.5, divisor=0.5),\n",
    "                    EnsureTyped(keys=all_keys, allow_missing_keys=True),\n",
    "                ])\n",
    "        return transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9c2364c-7388-4324-9579-f3a44e24ecdd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# train_ds = DataLoaderCamus(\n",
    "#     dataset_path='/mnt/datawow/lyq/dataset/CAMUS/',\n",
    "#     seg_parts = False,\n",
    "#         input_name=\"4CH_ED\",\n",
    "#         gt_name=\"4CH_ED_gt\",\n",
    "#         stage=\"train\",\n",
    "#     )\n",
    "\n",
    "# from monai.data import DataLoader\n",
    "# train_loader = DataLoader(data_loader, batch_size=2, shuffle=False, num_workers=1)\n",
    "# for x,y,_,_ in train_ds:\n",
    "#     print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28dce801-a03c-44f2-8f94-8fcf7939722d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "root = '/mnt/datawow/lyq/dataset/CAMUS/'\n",
    "data_type = '4CH_ED'\n",
    "labels = glob(root+f'database_nifti/*/*_{data_type}_gt.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cdd32dc3-b034-42a2-bc6c-c48b0c2457c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0047/patient0047_4CH_ED_gt.nii.gz',\n",
       " '/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0082/patient0082_4CH_ED_gt.nii.gz',\n",
       " '/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0381/patient0381_4CH_ED_gt.nii.gz',\n",
       " '/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0302/patient0302_4CH_ED_gt.nii.gz',\n",
       " '/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0188/patient0188_4CH_ED_gt.nii.gz']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7e71d15d-5eac-475c-9336-6adcffabc0a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0047/patient0047_4CH_ED_gt.nii.gz (541, 422)\n",
      "/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0082/patient0082_4CH_ED_gt.nii.gz (665, 519)\n",
      "/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0381/patient0381_4CH_ED_gt.nii.gz (551, 454)\n",
      "/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0302/patient0302_4CH_ED_gt.nii.gz (540, 422)\n",
      "/mnt/datawow/lyq/dataset/CAMUS/database_nifti/patient0188/patient0188_4CH_ED_gt.nii.gz (591, 487)\n"
     ]
    }
   ],
   "source": [
    "import nibabel as nib\n",
    "for i in labels[:5]:\n",
    "    arr = nib.load(i).get_fdata()\n",
    "    print(i, arr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8412c799-ab70-4221-a79b-344d672c8c4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = [i.replace('_gt','') for i in labels]\n",
    "\n",
    "data_dicts = [{\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(images, labels)]\n",
    "train_num = int(len(data_dicts)*0.7)\n",
    "train_files, val_files = data_dicts[:-train_num], data_dicts[-train_num:]\n",
    "\n",
    "all_keys = ['image', 'label']\n",
    "spatial_size=(328,328)\n",
    "crop_size=(256,256)\n",
    "label_list = [1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f626c5f-b24b-4879-af72-609bba25a7ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RetainSpecificMaskValues(MapTransform):\n",
    "    def __init__(self, keys, value_list, allow_missing_keys=False):\n",
    "        super().__init__(keys, allow_missing_keys)\n",
    "        self.value_list = value_list\n",
    "\n",
    "    def __call__(self, data):\n",
    "        d = dict(data)\n",
    "        for key in self.keys:\n",
    "            mask = np.isin(d[key], self.value_list)\n",
    "            d[key] = np.where(mask, d[key], 0)\n",
    "        return d\n",
    "\n",
    "train_transforms = Compose([\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        RetainSpecificMaskValues(keys=['label'], value_list=label_list),\n",
    "        EnsureChannelFirstd(keys=all_keys,channel_dim='no_channel' ),\n",
    "        Resized(keys=all_keys, spatial_size=spatial_size, allow_missing_keys=True, mode='nearest'),\n",
    "        RandSpatialCropd(all_keys, crop_size, random_size=False, allow_missing_keys=True),\n",
    "        #ScaleIntensityRangePercentilesd(keys=all_keys, lower=5, upper=95, b_min=0., b_max=1., allow_missing_keys=True) if len(all_keys)>0 else Identity(),\n",
    "        #NormalizeIntensityd(keys=all_keys, subtrahend=0.5, divisor=0.5),\n",
    "        EnsureTyped(keys=all_keys, allow_missing_keys=True),\n",
    "    ])\n",
    "\n",
    "val_transforms = Compose([\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        RetainSpecificMaskValues(keys=['label'], value_list=label_list),\n",
    "        EnsureChannelFirstd(keys=all_keys,channel_dim='no_channel' ),\n",
    "        Resized(keys=all_keys, spatial_size=spatial_size, allow_missing_keys=True, mode='nearest'),\n",
    "        # RandSpatialCropd(all_keys, crop_size, random_size=False, allow_missing_keys=True),\n",
    "        #ScaleIntensityRangePercentilesd(keys=all_keys, lower=5, upper=95, b_min=0., b_max=1., allow_missing_keys=True) if len(all_keys)>0 else Identity(),\n",
    "        #NormalizeIntensityd(keys=all_keys, subtrahend=0.5, divisor=0.5),\n",
    "        EnsureTyped(keys=all_keys, allow_missing_keys=True),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3e88518-7fcf-4a2d-93c2-b390608390d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 328, 328]) torch.Size([1, 1, 328, 328]) (array([0., 1., 2.], dtype=float32), array([87927, 11887,  7770]))\n",
      "torch.Size([1, 1, 328, 328]) torch.Size([1, 1, 328, 328]) (array([0., 1., 2.], dtype=float32), array([77819, 16147, 13618]))\n",
      "torch.Size([1, 1, 328, 328]) torch.Size([1, 1, 328, 328]) (array([0., 1., 2.], dtype=float32), array([82508, 12720, 12356]))\n",
      "torch.Size([1, 1, 328, 328]) torch.Size([1, 1, 328, 328]) (array([0., 1., 2.], dtype=float32), array([84090, 13454, 10040]))\n",
      "torch.Size([1, 1, 328, 328]) torch.Size([1, 1, 328, 328]) (array([0., 1., 2.], dtype=float32), array([94838,  7090,  5656]))\n"
     ]
    }
   ],
   "source": [
    "# train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0, num_workers=4)\n",
    "train_ds = Dataset(data=train_files, transform=train_transforms)\n",
    "\n",
    "# use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "# to generate 2 x 4 images for network training\n",
    "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2)\n",
    "\n",
    "# val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0, num_workers=4)\n",
    "val_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=1, num_workers=4)\n",
    "\n",
    "step=0\n",
    "for batch_data in val_loader:\n",
    "    step+=1\n",
    "    inputs, labels = (\n",
    "        batch_data[\"image\"],\n",
    "        batch_data[\"label\"],\n",
    "    )\n",
    "    print(inputs.shape, labels.shape, np.unique(labels,return_counts=True))\n",
    "    if step==5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89397be2-17bb-4b17-a19d-2b08a6753828",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_list = [1,2,3]\n",
    "device = torch.device(\"cuda:0\")\n",
    "device = torch.device(\"cpu\")\n",
    "\n",
    "num_classes = len(label_list)+1 # 选定的label个数+背景0\n",
    "UNet_meatdata = {\n",
    "    \"spatial_dims\": 2,\n",
    "    \"in_channels\": 1,\n",
    "    \"out_channels\": num_classes,\n",
    "    \"strides\": (2, 2, 2, 2),\n",
    "    \"num_res_units\": 2,\n",
    "    \"channels\":(4, 8, 16, 32, 64),\n",
    "    \"norm\": Norm.BATCH,\n",
    "}\n",
    "dataset_name = 'CAMUS'\n",
    "model = UNet(**UNet_meatdata).to(device)\n",
    "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "loss_type = \"DiceLoss\"\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "dice_metric = DiceMetric(include_background=False, reduction=\"mean\")\n",
    "model_name = f'{dataset_name}_{model.__class__.__name__}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53699160-7d32-4d86-b9bf-e04266707ad0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/600\n",
      "1/75, train_loss: 0.8254\n",
      "2/75, train_loss: 0.8169\n",
      "3/75, train_loss: 0.7996\n",
      "4/75, train_loss: 0.8078\n",
      "5/75, train_loss: 0.8052\n",
      "6/75, train_loss: 0.7995\n",
      "7/75, train_loss: 0.8108\n",
      "8/75, train_loss: 0.7902\n",
      "9/75, train_loss: 0.8221\n",
      "10/75, train_loss: 0.7940\n",
      "11/75, train_loss: 0.8107\n",
      "12/75, train_loss: 0.7821\n",
      "13/75, train_loss: 0.8131\n",
      "14/75, train_loss: 0.7930\n",
      "15/75, train_loss: 0.8182\n",
      "16/75, train_loss: 0.7992\n",
      "17/75, train_loss: 0.7830\n",
      "18/75, train_loss: 0.8015\n",
      "19/75, train_loss: 0.7868\n",
      "20/75, train_loss: 0.7899\n",
      "21/75, train_loss: 0.8163\n",
      "22/75, train_loss: 0.7850\n",
      "23/75, train_loss: 0.7866\n",
      "24/75, train_loss: 0.8076\n",
      "25/75, train_loss: 0.8009\n",
      "26/75, train_loss: 0.8175\n",
      "27/75, train_loss: 0.8078\n",
      "28/75, train_loss: 0.7949\n",
      "29/75, train_loss: 0.8055\n",
      "30/75, train_loss: 0.7909\n",
      "31/75, train_loss: 0.8127\n",
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      "epoch 1 average loss: 0.7960\n",
      "----------\n",
      "epoch 2/600\n",
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      "epoch 4 average loss: 0.7394\n",
      "----------\n",
      "epoch 5/600\n",
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      "epoch 5 average loss: 0.7078\n",
      "saved new best metric model at the 5th epoch\n",
      "current epoch: 5 current mean dice: 0.3199 \n",
      "best mean dice: 0.3199  at epoch: 5\n",
      "----------\n",
      "epoch 6/600\n",
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      "epoch 6 average loss: 0.6800\n",
      "----------\n",
      "epoch 7/600\n",
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      "epoch 7 average loss: 0.6508\n",
      "----------\n",
      "epoch 8/600\n",
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      "epoch 8 average loss: 0.6289\n",
      "----------\n",
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      "epoch 9 average loss: 0.6094\n",
      "----------\n",
      "epoch 10/600\n",
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      "epoch 10 average loss: 0.5938\n",
      "saved new best metric model at the 10th epoch\n",
      "current epoch: 10 current mean dice: 0.4151 \n",
      "best mean dice: 0.4151  at epoch: 10\n",
      "----------\n",
      "epoch 11/600\n",
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      "epoch 11 average loss: 0.5821\n",
      "----------\n",
      "epoch 12/600\n",
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      "epoch 12 average loss: 0.5687\n",
      "----------\n",
      "epoch 13/600\n",
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      "epoch 13 average loss: 0.5564\n",
      "----------\n",
      "epoch 14/600\n",
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      "----------\n",
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      "epoch 15 average loss: 0.5149\n",
      "saved new best metric model at the 15th epoch\n",
      "current epoch: 15 current mean dice: 0.4347 \n",
      "best mean dice: 0.4347  at epoch: 15\n",
      "----------\n",
      "epoch 16/600\n",
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      "epoch 16 average loss: 0.4916\n",
      "----------\n",
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      "epoch 17 average loss: 0.4708\n",
      "----------\n",
      "epoch 18/600\n",
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      "epoch 18 average loss: 0.4344\n",
      "----------\n",
      "epoch 19/600\n",
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      "epoch 19 average loss: 0.4078\n",
      "----------\n",
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      "epoch 20 average loss: 0.3904\n",
      "saved new best metric model at the 20th epoch\n",
      "current epoch: 20 current mean dice: 0.5329 \n",
      "best mean dice: 0.5329  at epoch: 20\n",
      "----------\n",
      "epoch 21/600\n",
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      "epoch 21 average loss: 0.3780\n",
      "----------\n",
      "epoch 22/600\n",
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      "epoch 22 average loss: 0.3690\n",
      "----------\n",
      "epoch 23/600\n",
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      "epoch 23 average loss: 0.3626\n",
      "----------\n",
      "epoch 24/600\n",
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      "epoch 24 average loss: 0.3559\n",
      "----------\n",
      "epoch 25/600\n",
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      "epoch 25 average loss: 0.3542\n",
      "saved new best metric model at the 25th epoch\n",
      "current epoch: 25 current mean dice: 0.5780 \n",
      "best mean dice: 0.5780  at epoch: 25\n",
      "----------\n",
      "epoch 26/600\n",
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      "epoch 26 average loss: 0.3537\n",
      "----------\n",
      "epoch 27/600\n",
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      "epoch 27 average loss: 0.3543\n",
      "----------\n",
      "epoch 28/600\n",
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      "epoch 28 average loss: 0.3443\n",
      "----------\n",
      "epoch 29/600\n",
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      "epoch 29 average loss: 0.3436\n",
      "----------\n",
      "epoch 30/600\n",
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      "epoch 30 average loss: 0.3420\n",
      "current epoch: 30 current mean dice: 0.5286 \n",
      "best mean dice: 0.5780  at epoch: 25\n",
      "----------\n",
      "epoch 31/600\n",
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      "epoch 31 average loss: 0.3416\n",
      "----------\n",
      "epoch 32/600\n",
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      "epoch 32 average loss: 0.3394\n",
      "----------\n",
      "epoch 33/600\n",
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      "epoch 33 average loss: 0.3385\n",
      "----------\n",
      "epoch 34/600\n",
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      "epoch 34 average loss: 0.3393\n",
      "----------\n",
      "epoch 35/600\n",
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      "epoch 35 average loss: 0.3368\n",
      "saved new best metric model at the 35th epoch\n",
      "current epoch: 35 current mean dice: 0.5823 \n",
      "best mean dice: 0.5823  at epoch: 35\n",
      "----------\n",
      "epoch 36/600\n",
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      "epoch 36 average loss: 0.3323\n",
      "----------\n",
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      "epoch 37 average loss: 0.3333\n",
      "----------\n",
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      "epoch 38 average loss: 0.3320\n",
      "----------\n",
      "epoch 39/600\n",
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      "epoch 39 average loss: 0.3342\n",
      "----------\n",
      "epoch 40/600\n",
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      "epoch 40 average loss: 0.3292\n",
      "current epoch: 40 current mean dice: 0.5692 \n",
      "best mean dice: 0.5823  at epoch: 35\n",
      "----------\n",
      "epoch 41/600\n",
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      "----------\n",
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      "epoch 42 average loss: 0.3271\n",
      "----------\n",
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      "epoch 43 average loss: 0.3295\n",
      "----------\n",
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      "epoch 44 average loss: 0.3265\n",
      "----------\n",
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      "epoch 45 average loss: 0.3276\n",
      "current epoch: 45 current mean dice: 0.5236 \n",
      "best mean dice: 0.5823  at epoch: 35\n",
      "----------\n",
      "epoch 46/600\n",
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      "epoch 46 average loss: 0.3292\n",
      "----------\n",
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      "epoch 47 average loss: 0.3244\n",
      "----------\n",
      "epoch 48/600\n",
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      "epoch 48 average loss: 0.3240\n",
      "----------\n",
      "epoch 49/600\n",
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      "epoch 49 average loss: 0.3233\n",
      "----------\n",
      "epoch 50/600\n",
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      "epoch 50 average loss: 0.3221\n",
      "current epoch: 50 current mean dice: 0.5776 \n",
      "best mean dice: 0.5823  at epoch: 35\n",
      "----------\n",
      "epoch 51/600\n",
      "1/75, train_loss: 0.3243\n",
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      "epoch 51 average loss: 0.3218\n",
      "----------\n",
      "epoch 52/600\n",
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      "epoch 52 average loss: 0.3224\n",
      "----------\n",
      "epoch 53/600\n",
      "1/75, train_loss: 0.3325\n",
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      "epoch 53 average loss: 0.3225\n",
      "----------\n",
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      "epoch 54 average loss: 0.3212\n",
      "----------\n",
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      "epoch 55 average loss: 0.3180\n",
      "current epoch: 55 current mean dice: 0.5094 \n",
      "best mean dice: 0.5823  at epoch: 35\n",
      "----------\n",
      "epoch 56/600\n",
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      "epoch 56 average loss: 0.3181\n",
      "----------\n",
      "epoch 57/600\n",
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      "epoch 57 average loss: 0.3188\n",
      "----------\n",
      "epoch 58/600\n",
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      "epoch 58 average loss: 0.3192\n",
      "----------\n",
      "epoch 59/600\n",
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      "epoch 59 average loss: 0.3238\n",
      "----------\n",
      "epoch 60/600\n",
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      "epoch 60 average loss: 0.3188\n",
      "current epoch: 60 current mean dice: 0.5673 \n",
      "best mean dice: 0.5823  at epoch: 35\n",
      "----------\n",
      "epoch 61/600\n",
      "1/75, train_loss: 0.3074\n",
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      "epoch 61 average loss: 0.3169\n",
      "----------\n",
      "epoch 62/600\n",
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      "epoch 62 average loss: 0.3174\n",
      "----------\n",
      "epoch 63/600\n",
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      "epoch 63 average loss: 0.3172\n",
      "----------\n",
      "epoch 64/600\n",
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      "epoch 64 average loss: 0.3166\n",
      "----------\n",
      "epoch 65/600\n",
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      "epoch 65 average loss: 0.3174\n",
      "saved new best metric model at the 65th epoch\n",
      "current epoch: 65 current mean dice: 0.5855 \n",
      "best mean dice: 0.5855  at epoch: 65\n",
      "----------\n",
      "epoch 66/600\n",
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      "epoch 66 average loss: 0.3155\n",
      "----------\n",
      "epoch 67/600\n",
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      "epoch 67 average loss: 0.3154\n",
      "----------\n",
      "epoch 68/600\n",
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      "epoch 68 average loss: 0.3156\n",
      "----------\n",
      "epoch 69/600\n",
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      "52/75, train_loss: 0.3412\n",
      "53/75, train_loss: 0.2974\n",
      "54/75, train_loss: 0.3291\n",
      "55/75, train_loss: 0.3380\n",
      "56/75, train_loss: 0.3205\n",
      "57/75, train_loss: 0.3068\n",
      "58/75, train_loss: 0.3062\n",
      "59/75, train_loss: 0.2882\n",
      "60/75, train_loss: 0.3029\n",
      "61/75, train_loss: 0.3105\n",
      "62/75, train_loss: 0.3120\n",
      "63/75, train_loss: 0.3197\n",
      "64/75, train_loss: 0.3536\n",
      "65/75, train_loss: 0.3147\n",
      "66/75, train_loss: 0.3089\n",
      "67/75, train_loss: 0.3473\n",
      "68/75, train_loss: 0.3172\n",
      "69/75, train_loss: 0.3632\n",
      "70/75, train_loss: 0.3059\n",
      "71/75, train_loss: 0.3028\n",
      "72/75, train_loss: 0.3264\n",
      "73/75, train_loss: 0.2946\n",
      "74/75, train_loss: 0.3413\n",
      "75/75, train_loss: 0.3261\n",
      "epoch 69 average loss: 0.3152\n",
      "----------\n",
      "epoch 70/600\n",
      "1/75, train_loss: 0.3019\n",
      "2/75, train_loss: 0.3110\n",
      "3/75, train_loss: 0.3249\n",
      "4/75, train_loss: 0.3078\n",
      "5/75, train_loss: 0.3288\n",
      "6/75, train_loss: 0.3147\n",
      "7/75, train_loss: 0.3084\n",
      "8/75, train_loss: 0.3276\n",
      "9/75, train_loss: 0.3312\n",
      "10/75, train_loss: 0.2928\n",
      "11/75, train_loss: 0.3161\n",
      "12/75, train_loss: 0.3174\n",
      "13/75, train_loss: 0.3263\n",
      "14/75, train_loss: 0.3334\n",
      "15/75, train_loss: 0.3007\n",
      "16/75, train_loss: 0.3019\n",
      "17/75, train_loss: 0.3010\n",
      "18/75, train_loss: 0.3102\n",
      "19/75, train_loss: 0.3016\n",
      "20/75, train_loss: 0.2926\n",
      "21/75, train_loss: 0.3036\n",
      "22/75, train_loss: 0.2974\n",
      "23/75, train_loss: 0.3132\n",
      "24/75, train_loss: 0.2956\n",
      "25/75, train_loss: 0.3087\n",
      "26/75, train_loss: 0.3159\n",
      "27/75, train_loss: 0.3008\n",
      "28/75, train_loss: 0.3387\n",
      "29/75, train_loss: 0.3169\n",
      "30/75, train_loss: 0.2971\n",
      "31/75, train_loss: 0.3240\n",
      "32/75, train_loss: 0.3177\n",
      "33/75, train_loss: 0.3021\n",
      "34/75, train_loss: 0.3353\n",
      "35/75, train_loss: 0.3104\n",
      "36/75, train_loss: 0.3147\n",
      "37/75, train_loss: 0.2998\n",
      "38/75, train_loss: 0.3151\n",
      "39/75, train_loss: 0.2941\n",
      "40/75, train_loss: 0.3473\n",
      "41/75, train_loss: 0.3127\n",
      "42/75, train_loss: 0.3185\n",
      "43/75, train_loss: 0.2973\n",
      "44/75, train_loss: 0.3171\n",
      "45/75, train_loss: 0.2915\n",
      "46/75, train_loss: 0.3028\n",
      "47/75, train_loss: 0.3175\n",
      "48/75, train_loss: 0.3275\n",
      "49/75, train_loss: 0.2987\n",
      "50/75, train_loss: 0.3134\n",
      "51/75, train_loss: 0.3073\n",
      "52/75, train_loss: 0.2943\n",
      "53/75, train_loss: 0.3412\n",
      "54/75, train_loss: 0.3404\n",
      "55/75, train_loss: 0.3471\n",
      "56/75, train_loss: 0.3154\n",
      "57/75, train_loss: 0.3044\n",
      "58/75, train_loss: 0.3305\n",
      "59/75, train_loss: 0.3175\n",
      "60/75, train_loss: 0.2960\n",
      "61/75, train_loss: 0.3010\n",
      "62/75, train_loss: 0.3366\n",
      "63/75, train_loss: 0.3272\n",
      "64/75, train_loss: 0.3057\n",
      "65/75, train_loss: 0.3418\n",
      "66/75, train_loss: 0.3317\n",
      "67/75, train_loss: 0.3332\n",
      "68/75, train_loss: 0.3000\n",
      "69/75, train_loss: 0.3199\n",
      "70/75, train_loss: 0.3243\n",
      "71/75, train_loss: 0.3029\n",
      "72/75, train_loss: 0.3406\n",
      "73/75, train_loss: 0.3167\n",
      "74/75, train_loss: 0.3040\n",
      "75/75, train_loss: 0.3010\n",
      "epoch 70 average loss: 0.3144\n"
     ]
    }
   ],
   "source": [
    "root_dir = '/mnt/datawow/liuyiquan/trained_model'\n",
    "max_epochs = 600\n",
    "val_interval = 5\n",
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "post_pred = Compose([AsDiscrete(argmax=True, to_onehot=num_classes)])\n",
    "post_label = Compose([AsDiscrete(to_onehot=num_classes)])\n",
    "roi_size = (128,128)\n",
    "slice_to_track = 30\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    print(\"-\" * 10)\n",
    "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "    for batch_data in train_loader:\n",
    "        step += 1\n",
    "        inputs, labels = (\n",
    "            batch_data[\"image\"].to(device),\n",
    "            batch_data[\"label\"].to(device),\n",
    "        )\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = loss_function(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size}, \" f\"train_loss: {loss.item():.4f}\")\n",
    "        # track batch loss metric\n",
    "        # aim_run.track(loss.item(), name=\"batch_loss\", context={\"type\": loss_type})\n",
    "\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append(epoch_loss)\n",
    "\n",
    "    # track epoch loss metric\n",
    "    # aim_run.track(epoch_loss, name=\"epoch_loss\", context={\"type\": loss_type})\n",
    "\n",
    "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        # if (epoch + 1) % val_interval * 2 == 0:\n",
    "        #     # track model params and gradients\n",
    "        #     track_params_dists(model, aim_run)\n",
    "        #     # THIS SEGMENT TAKES RELATIVELY LONG (Advise Against it)\n",
    "        #     track_gradients_dists(model, aim_run)\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for index, val_data in enumerate(val_loader):\n",
    "                val_inputs, val_labels = (\n",
    "                    val_data[\"image\"].to(device),\n",
    "                    val_data[\"label\"].to(device),\n",
    "                )\n",
    "\n",
    "                sw_batch_size = 4\n",
    "                val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                # val_outputs = model(val_inputs)\n",
    "                # tracking input, label and output images with Aim\n",
    "                # output = torch.argmax(val_outputs, dim=1)[0, :, :, slice_to_track].float()\n",
    "\n",
    "                # aim_run.track(\n",
    "                #     aim.Image(val_inputs[0, 0, :, :, slice_to_track], caption=f\"Input Image: {index}\"),\n",
    "                #     name=\"validation\",\n",
    "                #     context={\"type\": \"input\"},\n",
    "                # )\n",
    "                # aim_run.track(\n",
    "                #     aim.Image(val_labels[0, 0, :, :, slice_to_track], caption=f\"Label Image: {index}\"),\n",
    "                #     name=\"validation\",\n",
    "                #     context={\"type\": \"label\"},\n",
    "                # )\n",
    "                # aim_run.track(\n",
    "                #     aim.Image(output, caption=f\"Predicted Label: {index}\"),\n",
    "                #     name=\"predictions\",\n",
    "                #     context={\"type\": \"labels\"},\n",
    "                # )\n",
    "\n",
    "                val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "                val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "                # compute metric for current iteration\n",
    "                dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "            # aggregate the final mean dice result\n",
    "            metric = dice_metric.aggregate().item()\n",
    "            # track val metric\n",
    "            # aim_run.track(metric, name=\"val_metric\", context={\"type\": loss_type})\n",
    "\n",
    "            # reset the status for next validation round\n",
    "            dice_metric.reset()\n",
    "\n",
    "            metric_values.append(metric)\n",
    "            if metric > best_metric:\n",
    "                best_metric = metric\n",
    "                best_metric_epoch = epoch + 1\n",
    "                torch.save(model.state_dict(), os.path.join(root_dir, f\"{model_name}_best_metric_model.pth\"))\n",
    "\n",
    "                best_model_log_message = f\"saved new best metric model at the {epoch+1}th epoch\"\n",
    "                # aim_run.track(aim.Text(best_model_log_message), name=\"best_model_log_message\", epoch=epoch + 1)\n",
    "                print(best_model_log_message)\n",
    "\n",
    "            message1 = f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "            message2 = f\"\\nbest mean dice: {best_metric:.4f} \"\n",
    "            message3 = f\"at epoch: {best_metric_epoch}\"\n",
    "\n",
    "            # aim_run.track(aim.Text(message1 + \"\\n\" + message2 + message3), name=\"epoch_summary\", epoch=epoch + 1)\n",
    "            print(message1, message2, message3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d491f24b-117a-415b-98eb-06dfa68c1ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = os.path.join(root_dir, f\"{model_name}_best_metric_model.pth\")\n",
    "checkpoint = torch.load(model_path)\n",
    "model.load_state_dict(checkpoint)\n",
    "model.eval()\n",
    "print(model_path, ' loaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d60d762b-b94c-48e2-ae0b-ea1de278067c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current epoch: 198 current mean dice: 0.6479 \n",
      "best mean dice: 0.6479  at epoch: 190\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    for index, val_data in enumerate(val_loader):\n",
    "        val_inputs, val_labels = (\n",
    "            val_data[\"image\"].to(device),\n",
    "            val_data[\"label\"].to(device),\n",
    "        )\n",
    "\n",
    "        sw_batch_size = 4\n",
    "        val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "        val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "        val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "        # compute metric for current iteration\n",
    "        dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "    # aggregate the final mean dice result\n",
    "    metric = dice_metric.aggregate().item()\n",
    "    # track val metric\n",
    "    # aim_run.track(metric, name=\"val_metric\", context={\"type\": loss_type})\n",
    "\n",
    "    # reset the status for next validation round\n",
    "    dice_metric.reset()\n",
    "\n",
    "    metric_values.append(metric)\n",
    "\n",
    "    message1 = f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "    message2 = f\"\\nbest mean dice: {best_metric:.4f} \"\n",
    "    message3 = f\"at epoch: {best_metric_epoch}\"\n",
    "\n",
    "    # aim_run.track(aim.Text(message1 + \"\\n\" + message2 + message3), name=\"epoch_summary\", epoch=epoch + 1)\n",
    "    print(message1, message2, message3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1d7baeb-5c23-4be0-9217-01c1d201b629",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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